Abstract: This workshop introduces users to the ins and outs of using Python for data science. From data cleaning to model building and visualizations, we'll take a closer look at how Python can be leveraged to build effective data science workflows. We will go through a series of short examples together, and attendees will have their hands on the keyboard to try out some code using Jupyter Notebooks. We will use the Python standard library and pandas to effectively clean data. We'll learn our way around numpy, scipy, and scikit-learn to answer questions with data. Finally, we'll look at using libraries like matplotlib and seaborn to visualize different aspects of problems. Some basic familiarity with Python and Jupyter Notebooks will be helpful but no experience beyond that is required.
Bio: Skipper is a Director of Data Science at Civis Analytics, a data science technology and advisory firm. He leads a team of data scientists from all walks of life from physicists and engineers to statisticians and computer scientists. He is an economist by training, and has a decade of experience working in the Python data community. He started and led the statsmodels Python project, was formerly on the core pandas team, and has contributed to many projects in Python data stack.